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dc.contributor.advisorGalpin, Ixent
dc.coverage.spatialColombiaspa
dc.creatorCarrillo Gelvez, Gerson Enrique
dc.date.accessioned2021-02-12T15:19:50Z
dc.date.available2021-02-12T15:19:50Z
dc.date.created2021-02-09
dc.identifier.urihttp://hdl.handle.net/20.500.12010/17243
dc.description.abstractEste artículo representa la información en bases de datos no relacionales, aprovechando los beneficios de escalabilidad, alta disponibilidad, resiliencia y facilidad en el desarrollo. A través de este documento se dan a conocer unos algoritmos proporcionados por el motor de bases de datos de grafos Neo4j para computar métricas de grafos, nodos y relaciones. En primer lugar, se consolida un conjunto de datos públicos tomado del sistema de ventas online de MercadoLibre. Posteriormente se modelan los datos obtenidos en un esquema de grafos teniendo como nodos los usuarios, que pueden ser vendedores o compradores, productos y sus características. Como siguiente paso, se aplican algoritmos que calculan métricas del grafo, sus nodos y relaciones, visualizando los resultados obtenidos. Para finalizar, se pretende identificar las categorías más importantes que se ofrecen, las comunidades y usuarios más influyentes.spa
dc.format.extent24 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherUniversidad de Bogotá Jorge Tadeo Lozanospa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.subjectGrafosspa
dc.titleAnalítica de grafos para identificar entidades relevantes y comunidades en MercadoLibrespa
dc.type.localTrabajo de grado de maestríaspa
dc.subject.lembBases de datosspa
dc.subject.lembAlgoritmosspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.subject.keywordScalabilityspa
dc.identifier.repourlhttp://expeditio.utadeo.edu.cospa
dc.creator.degreeMagíster en Ingeniería y Analítica de Datosspa
dc.publisher.programMaestría en Ingeniería y Analítica de Datosspa
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dc.description.hashtag#Basesdedatosspa
dc.description.hashtag#Grafosspa
dc.description.abstractenglishThis article represents the information in non-relational databases, in terms of scalability, high availability, resilience and ease of development. Through this document, some algorithms provided by the Neo4j graph database engine to compute graph, node and relationship metrics are employed. In this paper, we firstly consolidate a data set taken from the MercadoLibre online sales system. Subsequently, the data is cast into a graph schema, with users as nodes, who can be sellers or buyers, products, and their characteristics. Algorithms are applied that calculate metrics of the graph, its nodes and relationships, displaying the results obtained. Finally, we seek to identify the most important categories offered, the most influential communities and users.spa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa


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